# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import unittest
from functools import partial
from itertools import product
from typing import TYPE_CHECKING, Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer

if TYPE_CHECKING:
    from collections.abc import Generator


class TrtConvertDepthwiseConv2dTransposeTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        if (
            inputs['input_data'].shape[1]
            != weights['conv2d_weight'].shape[1] * attrs[0]['groups']
        ):
            return False

        if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1]:
            return False

        if inputs['input_data'].shape[1] != attrs[0]['groups']:
            return False

        if attrs[0]['dilations'][0] != 1 or attrs[0]['dilations'][1] != 1:
            return False

        ver = paddle_infer.get_trt_compile_version()
        if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000:
            return False

        return True

    def sample_program_configs(self):
        self.trt_param.workspace_size = 1073741824

        def generate_input1(batch, attrs: list[dict[str, Any]]):
            return np.ones([batch, attrs[0]['groups'], 64, 64]).astype(
                np.float32
            )

        def generate_weight1(attrs: list[dict[str, Any]]):
            return np.random.random([attrs[0]['groups'], 1, 3, 3]).astype(
                np.float32
            )

        for (
            batch,
            strides,
            paddings,
            groups,
            padding_algorithm,
            dilations,
            data_format,
        ) in product(
            [1, 2, 4],
            [[1, 1], [2, 2], [1, 2]],
            [[0, 3], [1, 2, 3, 4]],
            [1, 2, 3],
            ['EXPLICIT', 'SAME', 'VALID'],
            [[1, 1], [2, 2], [1, 2]],
            ['NCHW'],
        ):
            dics = [
                {
                    "data_format": data_format,
                    "dilations": dilations,
                    "padding_algorithm": padding_algorithm,
                    "groups": groups,
                    "paddings": paddings,
                    "strides": strides,
                    "output_size": [],
                    "output_padding": [],
                }
            ]

            ops_config = [
                {
                    "op_type": "conv2d_transpose",
                    "op_inputs": {
                        "Input": ["input_data"],
                        "Filter": ["conv2d_weight"],
                    },
                    "op_outputs": {"Output": ["output_data"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={
                    "conv2d_weight": TensorConfig(
                        data_gen=partial(generate_weight1, dics)
                    )
                },
                inputs={
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, batch, dics)
                    )
                },
                outputs=["output_data"],
            )

            yield program_config

    def sample_predictor_configs(
        self, program_config
    ) -> Generator[
        Any, Any, tuple[paddle_infer.Config, list[int], float] | None
    ]:
        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {
                "input_data": [1, attrs[0]['groups'], 32, 32],
                "output_data": [1, attrs[0]['groups'], 32, 32],
            }
            self.dynamic_shape.max_input_shape = {
                "input_data": [4, attrs[0]['groups'], 64, 64],
                "output_data": [4, attrs[0]['groups'], 64, 64],
            }
            self.dynamic_shape.opt_input_shape = {
                "input_data": [1, attrs[0]['groups'], 64, 64],
                "output_data": [1, attrs[0]['groups'], 64, 64],
            }

        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            return 1, 2

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, False),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, False),
            (1e-3, 1e-3),
        )
        # self.trt_param.precision = paddle_infer.PrecisionType.Int8
        # yield self.create_inference_config(), generate_trt_nodes_num(
        #     attrs, False), (1e-5, 1e-5)

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            (1e-3, 1e-3),
        )
        # self.trt_param.precision = paddle_infer.PrecisionType.Int8
        # yield self.create_inference_config(), generate_trt_nodes_num(
        #     attrs, True), (1e-5, 1e-5)

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if self.trt_param.precision == paddle_infer.PrecisionType.Int8:
                return True
            return False

        self.add_skip_case(
            teller1,
            SkipReasons.TRT_NOT_IMPLEMENTED,
            "When precisionType is int8 without relu op, output is different between Trt and Paddle.",
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test()

    def test_quant(self):
        self.add_skip_trt_case()
        self.run_test(quant=True)


if __name__ == "__main__":
    unittest.main()
